Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Neuro-symbolic data mining has emerged as a promising paradigm that integrates neural learning with symbolic reasoning to enable hybrid intelligence systems. Traditional data mining techniques based purely on statistical or neural models often lack interpretability and logical consistency, while symbolic approaches struggle with scalability and noisy data. Neuro-symbolic methods combine the strengths of both paradigms, allowing models to learn from data while also respecting structured knowledge and logical constraints. This paper reviews the concepts, architectures, algorithms, and applications of neuro-symbolic data mining for hybrid reasoning. It discusses knowledge representation, neural symbolic integration strategies, reasoning mechanisms, and learning frameworks. A comparative analysis of existing approaches is presented along with challenges such as explainability, knowledge acquisition, and computational complexity. The study concludes that neuro-symbolic data mining can significantly enhance decision-making systems in domains such as healthcare, robotics, engineering analytics, and intelligent automation.

Keywords: Neuro-symbolic learning, hybrid reasoning, knowledge mining, explainable AI, logical constraints, intelligent data mining